Potts Hamiltonian Models and Molecular Dynamics Free Energy Simulations for Predicting the Impact of Mutations on Protein Kinase Stability.
Potts Hamiltonian Models and Molecular Dynamics Free Energy Simulations for Predicting the Impact of Mutations on Protein Kinase Stability.
复制标题
用于预测突变对蛋白激酶稳定性影响的波茨哈密顿模型和分子动力学自由能模拟。
DOI：
10.1021/acs.jpcb.3c08097
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Levy,RonaldM
中科院分区：
文献类型：
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作者：
Thakur,Abhishek;Gizzio,Joan;Levy,RonaldM
激酶蛋白中的单点突变会影响其稳定性和适应性，对这些影响的计算分析可以为该酶家族的蛋白质序列，结构和功能之间的关系提供见解。为了评估突变对蛋白质稳定性的影响，我们使用基于序列的Potts Hamilton模型在激酶家族多序列比对（MSA）上训练，以计算突变的统计能量（适应度）效应，并将其与在显式溶剂中通过全原子分子动力学自由能扰动（FEP）模拟计算的相对折叠自由能（ΔΔGs）进行比较。Potts模型中突变的适应性效应（ΔEs）与实验热稳定性数据（Pearsonr= 0.68）显示出良好的一致性，与我们观察到的基于结构的相对FEP模拟预测的ΔΔGs的相关性相似。认识到使用Potts模型快速估计癌症基因组学数据中激酶突变的蛋白质稳定性影响的可能优势，我们使用Potts统计能量模型估计了基因组数据共享（GDC）数据库中报告的三种不同激酶（Wee1，E11和Cdc7）的65个保守和非保守突变的稳定性影响。根据Potts模型计算的这些突变的ΔEs与FEP模拟的相应ΔΔGs一致（Pearson比率为0.72）。这些方法之间的协议表明，Potts模型可用作基于序列的工具，用于高通量筛选突变效应，作为预测突变稳定性效应的计算管道的一部分。我们还演示了如何基于健身的Potts模型计算的可扩展性允许使用FEP模拟不容易访问的分析。为此，我们在Potts模型中采用了位点饱和诱变，以研究在不同癌症进化情况下看到的突变的相对稳定性效应。我们使用这种方法来分析药物压力在Abl激酶中的作用，通过比较在各种癌症类型中观察到的体细胞突变的相对适应性罚分与计算的与癌症耐药性相关的突变的相对适应性罚分。我们观察到，与在各种肿瘤中观察到的Abl的体细胞突变相反，这些体细胞突变似乎已经中性地进化，在靶向治疗中在药物压力下进化的癌症突变倾向于保持酶的稳定性。
Single-point mutations in kinase proteins can affect their stability and fitness, and computational analysis of these effects can provide insights into the relationships among protein sequence, structure, and function for this enzyme family. To assess the impact of mutations on protein stability, we used a sequence-based Potts Hamiltonian model trained on a kinase family multiple-sequence alignment (MSA) to calculate the statistical energy (fitness) effects of mutations and compared these against relative folding free energies (ΔΔGs) calculated from all-atom molecular dynamics free energy perturbation (FEP) simulations in explicit solvent. The fitness effects of mutations in the Potts model (ΔEs) showed good agreement with experimental thermostability data (Pearsonr= 0.68), similar to the correlation we observed with ΔΔGs predicted from structure-based relative FEP simulations. Recognizing the possible advantages of using Potts models to rapidly estimate protein stability effects of kinase mutations seen in cancer genomics data, we used the Potts statistical energy model to estimate the stability effects of 65 conservative and nonconservative mutations across three distinct kinases (Wee1, Abl1, and Cdc7) with somatic mutations reported in the Genomic Data Commons (GDC) database. The ΔEs of these mutations calculated from the Potts model are consistent with the corresponding ΔΔGs from FEP simulations (Pearson ratio of 0.72). The agreement between these methods suggests that the Potts model may be used as a sequence-based tool for high-throughput screening of mutational effects as part of a computational pipeline for predicting the stability effects of mutations. We also demonstrate how the scalability of the fitness-based Potts model calculations permits analyses that are not easily accessed using FEP simulations. To this end, we employed site-saturation mutagenesis in the Potts model in order to investigate the relative stability effects of mutations seen in different cancer evolutionary scenarios. We used this approach to analyze the effects of drug pressure in Abl kinase by contrasting the relative fitness penalties of somatic mutations seen in miscellaneous cancer types with those calculated for mutations associated with cancer drug resistance. We observed that, in contrast to somatic mutations of Abl seen in various tumors that appear to have evolved neutrally, cancer mutations that evolved under drug pressure in Abl-targeted therapies tend to preserve enzyme stability.